Book Review

Review of: ANN RAPPAPORT & MARGARET FRESHER FLAHERTY, CORPORATE RESPONSES TO ENVIRONMENTAL CHALLENGES: INITIATIVES BY MULTINATIONAL MANAGEMENT. (Quorum Books 1992) [186 pp.] Acknowledgements, bibliography, figures, foreword, index, notes, tables. LC: 91-44706; ISBN: 0-89930-715-9 [Cloth $45.00.

Book Review

Review of the following: BIOMEDICAL POLITICS. (Kathi E. Hanna, ed., National Academy Press 1991) [352 pp.] Preface and acknowledgements, Carl W. Gottschalk, Chair, Institute of Medicine Committee to Study Biomedical Decision Making. Appendices, biographical notes on authors and commentators, index, notes, references. LC 91- 18394, ISBN 0-309-04486-3. [Cloth $29.95. Export$36.00. 2101 Constitution Ave., NW, Washington DC 20418.

Promoting and Managing Genome Innovation

An introduction to the symposium, Promoting and Managing Genome Innovation held October 1995. The conference was organized by Professor Thomas G. Field, Jr. and Gianna Julian-Arnold. The conference was funded in part by the Ethical, Legal and Social Issues component of the D.O.E. Human Genome Program; Nixon, Hargrave, Devans & Doyle L.L.P., Rochester, N.Y.; and Human Genome Sciences

Book Review

Review of the book: ENVIRONMENTAL POLITICS: PUBLIC COSTS, PRIVATE REWARDS. (Michael S. Greve & Fred L. Smith, Jr. eds., Praeger 1992) [209 pp.] Acknowledgements, biographical information, figures, foreword by James Q. Wilson, index, notes, selected bibliography, tables. LC 91-44009, ISBN 0-275- 94238-4. [Paper $19.95, cloth$45.00. One Madison Avenue, New York N.Y. 10010.

Replacing Neural Networks by Optimal Analytical Predictors for the Detection of Phase Transitions

Identifying phase transitions and classifying phases of matter is central to understanding the properties and behavior of a broad range of material systems. In recent years, machine-learning (ML) techniques have been successfully applied to perform such tasks in a data-driven manner. However, the success of this approach notwithstanding, we still lack a clear understanding of ML methods for detecting phase transitions, particularly of those that utilize neural networks (NNs). In this work, we derive analytical expressions for the optimal output of three widely used NN-based methods for detecting phase transitions. These optimal predictions correspond to the results obtained in the limit of high model capacity. Therefore, in practice, they can, for example, be recovered using sufficiently large, well-trained NNs. The inner workings of the considered methods are revealed through the explicit dependence of the optimal output on the input data. By evaluating the analytical expressions, we can identify phase transitions directly from experimentally accessible data without training NNs, which makes this procedure favorable in terms of computation time. Our theoretical results are supported by extensive numerical simulations covering, e.g., topological, quantum, and many-body localization phase transitions. We expect similar analyses to provide a deeper understanding of other classification tasks in condensed matter physics

Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions

Machine learning-based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested. The models are based on function-, kernel- and grid-based representations of the reactant and product state distributions. While all three methods predict final state distributions from explicit quasi-classical trajectory simulations with R$^2$ > 0.998, the grid-based approach performs best. Although a function-based approach is found to be more than two times better in computational performance, the kernel- and grid-based approaches are preferred in terms of prediction accuracy, practicability and generality. The function-based approach also suffers from lacking a general set of model functions. Applications of the grid-based approach to nonequilibrium, multi-temperature initial state distributions are presented, a situation common to energy distributions in hypersonic flows. The role of such models in Direct Simulation Monte Carlo and computational fluid dynamics simulations is also discussed

Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion

Machine learning has been successfully used to study phase transitions. One of the most popular approaches to identifying critical points from data without prior knowledge of the underlying phases is the learning-by-confusion scheme. As input, it requires system samples drawn from a grid of the parameter whose change is associated with potential phase transitions. Up to now, the scheme required training a distinct binary classifier for each possible splitting of the grid into two sides, resulting in a computational cost that scales linearly with the number of grid points. In this work, we propose and showcase an alternative implementation that only requires the training of a single multi-class classifier. Ideally, such multi-task learning eliminates the scaling with respect to the number of grid points. In applications to the Ising model and an image dataset generated with Stable Diffusion, we find significant speedups that closely correspond to the ideal case, with only minor deviations.Comment: 7 pages, 3 figures, Machine Learning and the Physical Sciences Workshop, NeurIPS 202

Piranti Lunak Untuk Mendesain Program Dalam Bahasa Pemrograman C Berdasarkan Hoare Logic

The purpose of Hoare Logic is to provide a set of logical rules in order to reason about the correctness of computer programs with the rigor of mathematical logic. Because of that, Hoare Logic becomes the axiomatic basis for computer programming with several rules to prove the correctness of program. Hence, we can apply the proven rules of Hoare Logic as the basis to design a program correctly according to Hoare Logic. In this paper, Hoare Logic is applied in a software which is designed to help the user to design a program in C programming language correctly based on rules in Hoare Logic. When using this software, the user needs to know what program he will create and analyze an algorithm for it. After that, the user can use the software containing the rules of Hoare Logic and write the pseudo-code of C to design his program. At the end of this application, the user will obtain a source code of the program written in C programming language. This software is guaranteed to produce 100% correct output only if the users have the basic understanding of Hoare Logic as well as C program language before using this software

Interpretable and unsupervised phase classification

Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method for phase classification which is rendered interpretable via an analytical derivation of its optimal predictions and allows for an automated construction scheme for order parameters. Based on these findings, we propose and apply an alternative, physically-motivated, data-driven scheme which relies on the difference between mean input features. This mean-based method is computationally cheap and directly interpretable. As an example, we consider the physically rich ground-state phase diagram of the spinless Falicov-Kimball model.Comment: 6+12 pages, 3+7 figure